806 citations · 5.4k across the 44 of their papers we have counts for
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Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
Louis Schatzki, Martin Larocca, Quynh T. Nguyen +2
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential. For instance, models based on qu…
Theory for Equivariant Quantum Neural Networks
Quynh T. Nguyen, Louis Schatzki, Paolo Braccia +5
Quantum neural network architectures that have little-to-no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakth…
Representation Theory for Geometric Quantum Machine Learning
Michael Ragone, Paolo Braccia, Quynh T. Nguyen +5
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance. Importatio…
Building spatial symmetries into parameterized quantum circuits for faster training
Frederic Sauvage, Martin Larocca, Patrick J. Coles +1
Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates emplo…
Inference-Based Quantum Sensing
C. Huerta Alderete, Max Hunter Gordon, Frederic Sauvage +4
In a standard Quantum Sensing (QS) task one aims at estimating an unknown parameter , encoded into an -qubit probe state, via measurements of the system. The success of this…
The battle of clean and dirty qubits in the era of partial error correction
Daniel Bultrini, Samson Wang, Piotr Czarnik +4
When error correction becomes possible it will be necessary to dedicate a large number of physical qubits to each logical qubit. Error correction allows for deeper circuits to be r…
Group-Invariant Quantum Machine Learning
Martin Larocca, Frederic Sauvage, Faris M. Sbahi +3
Quantum Machine Learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., w…
Covariance matrix preparation for quantum principal component analysis
Max Hunter Gordon, M. Cerezo, Lukasz Cincio +1
Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset. Recently, quantum algorit…